{"id":77479,"date":"2026-02-24T19:50:14","date_gmt":"2026-02-24T11:50:14","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/77479.html"},"modified":"2026-02-24T19:50:14","modified_gmt":"2026-02-24T11:50:14","slug":"%e5%88%86%e4%ba%ab%e4%b8%80%e5%a5%97%e9%94%8b%e5%93%a5%e5%8e%9f%e5%88%9b%e7%9a%84ai%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%be%ae%e8%b0%83%e8%ae%ad%e7%bb%83-%e5%be%ae%e5%8d%9a%e8%88%86%e6%83%85%e5%88%86","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/77479.html","title":{"rendered":"\u5206\u4eab\u4e00\u5957\u950b\u54e5\u539f\u521b\u7684AI\u5927\u6a21\u578b\u5fae\u8c03\u8bad\u7ec3 \u5fae\u535a\u8206\u60c5\u5206\u6790\u53ef\u89c6\u5316\u7cfb\u7edf(pytorch2+\u57fa\u4e8eBERT\u5927\u6a21\u578b\u8bad\u7ec3\u5fae\u8c03+flask+pandas+echarts)\uff0c\u975e\u5e38Nice\uff0c\u754c\u9762\u975e\u5e38\u597d\u770b"},"content":{"rendered":"

\u5927\u5bb6\u597d，\u6211\u662f\u950b\u54e5，\u6700\u8fd1\u5199\u4e86AI\u5927\u6a21\u578b\u5fae\u8c03\u8bad\u7ec3 \u5fae\u535a\u8206\u60c5\u5206\u6790\u53ef\u89c6\u5316\u7cfb\u7edf(pytorch2+\u57fa\u4e8eBERT\u5927\u6a21\u578b\u8bad\u7ec3\u5fae\u8c03+flask+pandas+echarts)，\u975e\u5e38Nice，\u754c\u9762\u975e\u5e38\u597d\u770b，\u5206\u4eab\u4e0b\u54c8\u3002<\/p>\n

\"\"<\/p>\n

\u9879\u76ee\u4ecb\u7ecd<\/h3>\n

\u672c\u6587\u8bbe\u8ba1\u5e76\u5b9e\u73b0\u4e86\u4e00\u4e2a\u57fa\u4e8eBERT\u5927\u6a21\u578b\u5fae\u8c03\u8bad\u7ec3\u7684\u5fae\u535a\u8206\u60c5\u5206\u6790\u53ef\u89c6\u5316\u7cfb\u7edf\u3002\u7cfb\u7edf\u91c7\u7528Python\u8bed\u8a00\u5f00\u53d1，\u540e\u7aef\u4f7f\u7528Flask Web\u6846\u67b6，\u524d\u7aef\u4f7f\u7528ECharts\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316，\u6570\u636e\u5e93\u91c7\u7528MySQL 8.0，\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u91c7\u7528PyTorch 2.x\u3002\u7cfb\u7edf\u4e3b\u8981\u5305\u542b\u4ee5\u4e0b\u529f\u80fd\u6a21\u5757：（1）\u5fae\u535a\u6570\u636e\u722c\u866b\u6a21\u5757，\u901a\u8fc7\u8bf7\u6c42\u5fae\u535aAPI\u63a5\u53e3\u81ea\u52a8\u91c7\u96c6\u5fae\u535a\u6587\u7ae0\u548c\u8bc4\u8bba\u6570\u636e；（2）\u6570\u636e\u5904\u7406\u4e0e\u5206\u8bcd\u6a21\u5757，\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u6e05\u6d17，\u4f7f\u7528jieba\u8fdb\u884c\u4e2d\u6587\u5206\u8bcd\u548c\u8bcd\u9891\u7edf\u8ba1；（3）\u57fa\u4e8eBERT\u5927\u6a21\u578b\u5fae\u8c03\u7684\u60c5\u611f\u5206\u6790\u6a21\u5757，\u5728BERT-base-chinese\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u57fa\u7840\u4e0a，\u51bb\u7ed3BERT\u53c2\u6570，\u6dfb\u52a0\u5168\u8fde\u63a5\u5206\u7c7b\u5c42，\u4f7f\u7528\u5fae\u535a\u60c5\u611f\u6807\u6ce8\u6570\u636e\u96c6\u8fdb\u884c\u589e\u91cf\u5fae\u8c03\u8bad\u7ec3，\u5b9e\u73b0\u6587\u672c\u60c5\u611f\u4e8c\u5206\u7c7b（\u6b63\u9762\/\u8d1f\u9762）；（4）\u6570\u636e\u53ef\u89c6\u5316\u6a21\u5757，\u4f7f\u7528ECharts\u5b9e\u73b0\u6298\u7ebf\u56fe\u3001\u997c\u72b6\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u8bcd\u4e91\u56fe\u3001\u4e2d\u56fd\u5730\u56fe\u7b49\u591a\u7ef4\u5ea6\u53ef\u89c6\u5316\u5c55\u793a\u3002<\/p>\n

\u6e90\u7801\u4e0b\u8f7d<\/h3>\n

\u94fe\u63a5：https:\/\/pan.baidu.com\/s\/1I1r7XgWUDWZa0XMvU2S5rQ?pwd=1234<\/span>
\n\u63d0\u53d6\u7801：1234<\/span><\/p>\n<\/p>\n

\u7cfb\u7edf\u5c55\u793a
\n\"\"<\/h3>\n

\"\"\"\"\"\"\"\"\"\"\"\"<\/p>\n

\u6838\u5fc3\u4ee3\u7801<\/h3>\n

import pandas as pd
\nfrom flask import Blueprint, render_template, jsonify, request
\nfrom snownlp import SnowNLP<\/p>\n

from dao import articleDao, commentDao
\nfrom llm.weibo import data_classfication
\nfrom llm.weibo_train import check_data, check_datas_batch
\nfrom util import wordcloudUtil, mapUtil<\/p>\n

pb = Blueprint('page', __name__, url_prefix='\/page', template_folder='templates')<\/p>\n

@pb.route('\/home')
\ndef home():
\n """
\n \u8fdb\u5165\u4e3b\u9875\u9762，\u83b7\u53d6\u76f8\u5e94\u7684\u6570\u636e，\u5e26\u5230\u9875\u9762\u53bb
\n :return:
\n """
\n articleData = articleDao.get7DayArticle()
\n xAxis7ArticleData = []
\n yAxis7ArticleData = []
\n for article in articleData:
\n xAxis7ArticleData.append(article[0])
\n yAxis7ArticleData.append(article[1])<\/p>\n

# \u83b7\u53d6\u5e16\u5b50\u7c7b\u522b\u6570\u91cf
\n arcTypeData = []
\n articleTypeAmountList = articleDao.getArticleTypeAmount()
\n for arcType in articleTypeAmountList:
\n arcTypeData.append({'value': arcType[1], 'name': arcType[0]})<\/p>\n

# \u83b7\u53d6top50\u8bc4\u8bba\u7528\u6237\u540d
\n top50CommentUserList = commentDao.getTopCommentUser()
\n top50CommentUserNameList = [cu[0] for cu in top50CommentUserList]
\n str = ' '.join(top50CommentUserNameList)
\n wordcloudUtil.genWordCloudPic(str, 'comment_mask.jpg', 'comment_user_cloud.jpg')<\/p>\n

# \u83b7\u53d67\u5929\u8bc4\u8bba\u6570\u91cf
\n commentData = []
\n commentAmountList = commentDao.getCommentAmount()
\n for comment in commentAmountList:
\n commentData.append({'value': comment[1], 'name': comment[0]})
\n return render_template('index.html',
\n xAxis7ArticleData=xAxis7ArticleData,
\n yAxis7ArticleData=yAxis7ArticleData,
\n arcTypeData=arcTypeData,
\n commentData=commentData)<\/p>\n

@pb.route('homePageData')
\ndef getHomePageData():
\n """
\n \u83b7\u53d6\u4e3b\u9875\u6570\u636e ajax\u5f02\u6b65\u4ea4\u4e92 \u524d\u7aef\u6bcf\u96945\u5206\u949f\u8bf7\u6c42\u4e00\u6b21 \u5b9e\u65f6\u6570\u636e
\n :return:
\n """
\n totalArticle = articleDao.getTotalArticle()
\n topAuthor = articleDao.getTopAuthor()
\n topRegion = articleDao.getTopRegion()
\n topArticles = articleDao.getArticleTopZan()
\n return jsonify(totalArticle=totalArticle, topAuthor=topAuthor, topRegion=topRegion, topArticles=topArticles)<\/p>\n

@pb.route('hotWord')
\ndef hotWord():
\n """
\n \u70ed\u8bcd\u5206\u6790\u7edf\u8ba1
\n :return:
\n """
\n hotwordList = []
\n # \u53ea\u8bfb\u53d6\u524d100\u6761
\n df = pd.read_csv('.\/fenci\/comment_fre.csv', nrows=100)
\n for value in df.values:
\n hotwordList.append(value[0])
\n # \u83b7\u53d6\u8bf7\u6c42\u53c2\u6570，\u5982\u679c\u6ca1\u6709\u83b7\u53d6\u5230，\u7ed9\u4e2a\u9ed8\u8ba4\u503c \u7b2c\u4e00\u4e2a\u5217\u8868\u6570\u636e
\n defaultHotWord = request.args.get('word', default=hotwordList[0])
\n hotwordNum = 0 # \u51fa\u73b0\u6b21\u6570
\n for value in df.values:
\n if defaultHotWord == value[0]:
\n hotwordNum = value[1]<\/p>\n

# \u60c5\u611f\u5206\u6790
\n sentiments = ''
\n # stc = SnowNLP(defaultHotWord).sentiments
\n # if stc > 0.6:
\n # sentiments = '\u6b63\u9762'
\n # elif stc < 0.2:
\n # sentiments = '\u8d1f\u9762'
\n # else:
\n # sentiments = '\u4e2d\u6027'
\n # \u4f7f\u7528\u5927\u6a21\u578b\u8fdb\u884c\u8206\u60c5\u5206\u6790
\n # sentiments = data_classfication(defaultHotWord)
\n # \u4f7f\u7528\u5927\u6a21\u578b\u5fae\u8c03\u8fdb\u884c\u8206\u60c5\u5206\u6790
\n sentiments = check_data([defaultHotWord])<\/p>\n

commentHotWordData = commentDao.getCommentHotWordAmount(defaultHotWord)
\n xAxisHotWordData = []
\n yAxisHotWordData = []
\n for comment in commentHotWordData:
\n xAxisHotWordData.append(comment[0])
\n yAxisHotWordData.append(comment[1])<\/p>\n

commentList = commentDao.getCommentByHotWord(defaultHotWord)
\n return render_template('hotWord.html',
\n hotwordList=hotwordList,
\n defaultHotWord=defaultHotWord,
\n hotwordNum=hotwordNum,
\n sentiments=sentiments,
\n xAxisHotWordData=xAxisHotWordData,
\n yAxisHotWordData=yAxisHotWordData,
\n commentList=commentList)<\/p>\n

@pb.route('articleData')
\ndef articleData():
\n """
\n \u5fae\u535a\u8206\u60c5\u5206\u6790
\n :return:
\n """
\n articleOldList = articleDao.getAllArticle()
\n # \u83b7\u53d6\u6240\u6709\u5e16\u5b50\u6807\u9898
\n articleTitleList = [article[1] for article in articleOldList]
\n # \u6279\u91cf\u4f7f\u7528\u5927\u6a21\u578b\u8fdb\u884c\u8206\u60c5\u5206\u6790
\n sentimentList = check_datas_batch(articleTitleList)
\n print(sentimentList)
\n # \u8ffd\u52a0\u60c5\u611f\u5206\u6790\u5c5e\u6027
\n articleNewList = []
\n for i in range(len(articleOldList)):
\n if sentimentList[i] == 1:
\n articleNewList.append(articleOldList[i] + ('\u6b63\u9762',))
\n else:
\n articleNewList.append(articleOldList[i] + ('\u8d1f\u9762',))
\n # for article in articleOldList:
\n # article = list(article)
\n # \u60c5\u611f\u5206\u6790
\n # sentiments = ''
\n # stc = SnowNLP(article[1]).sentiments
\n # if stc > 0.6:
\n # sentiments = '\u6b63\u9762'
\n # elif stc < 0.2:
\n # sentiments = '\u8d1f\u9762'
\n # else:
\n # sentiments = '\u4e2d\u6027'
\n # \u4f7f\u7528\u5927\u6a21\u578b\u8fdb\u884c\u8206\u60c5\u5206\u6790
\n # sentiments = data_classfication(article[1])
\n # \u4f7f\u7528\u5927\u6a21\u578b\u5fae\u8c03\u8fdb\u884c\u8206\u60c5\u5206\u6790
\n # sentiments = check_data([article[1]])
\n # article.append(sentiments)
\n # articleNewList.append(article)
\n return render_template('articleData.html', articleList=articleNewList)<\/p>\n

@pb.route('articleDataAnalysis')
\ndef articleDataAnalysis():
\n """
\n \u5fae\u535a\u6570\u636e\u5206\u6790
\n :return:
\n """
\n arcTypeList = []
\n df = pd.read_csv('.\/spider\/arcType_data.csv')
\n for value in df.values:
\n arcTypeList.append(value[0])
\n # \u83b7\u53d6\u8bf7\u6c42\u53c2\u6570，\u5982\u679c\u6ca1\u6709\u83b7\u53d6\u5230，\u7ed9\u4e2a\u9ed8\u8ba4\u503c \u7b2c\u4e00\u4e2a\u5217\u8868\u6570\u636e
\n defaultArcType = request.args.get('arcType', default=arcTypeList[0])
\n articleList = articleDao.getArticleByArcType(defaultArcType)
\n xDzData = [] # \u70b9\u8d5ex\u8f74\u6570\u636e
\n xPlData = [] # \u8bc4\u8bbax\u8f74\u6570\u636e
\n xZfData = [] # \u8f6c\u53d1x\u8f74\u6570\u636e
\n rangeNum = 1000
\n rangeNum2 = 100
\n for item in range(0, 10):
\n xDzData.append(str(rangeNum * item) + '-' + str(rangeNum * (item + 1)))
\n xPlData.append(str(rangeNum * item) + '-' + str(rangeNum * (item + 1)))
\n for item in range(0, 20):
\n xZfData.append(str(rangeNum2 * item) + '-' + str(rangeNum2 * (item + 1)))
\n xDzData.append('1\u4e07+')
\n xPlData.append('1\u4e07+')
\n xZfData.append('2\u5343+')
\n yDzData = [0 for x in range(len(xDzData))] # \u70b9\u8d5ey\u8f74\u6570\u636e
\n yPlData = [0 for x in range(len(xPlData))] # \u8bc4\u8bbay\u8f74\u6570\u636e
\n yZfData = [0 for x in range(len(xZfData))] # \u8f6c\u53d1y\u8f74\u6570\u636e
\n for article in articleList:
\n for item in range(len(xDzData)):
\n if int(article[4]) < rangeNum * (item + 1):
\n yDzData[item] += 1
\n break
\n elif int(article[4]) > 10000:
\n yDzData[len(xDzData) – 1] += 1
\n break
\n if int(article[3]) < rangeNum * (item + 1):
\n yPlData[item] += 1
\n break
\n elif int(article[3]) > 10000:
\n yPlData[len(xDzData) – 1] += 1
\n break<\/p>\n

for article in articleList:
\n for item in range(len(xZfData)):
\n if int(article[2]) < rangeNum2 * (item + 1):
\n yZfData[item] += 1
\n break
\n elif int(article[2]) > 2000:
\n yZfData[len(xZfData) – 1] += 1
\n break
\n return render_template('articleDataAnalysis.html',
\n arcTypeList=arcTypeList,
\n defaultArcType=defaultArcType,
\n xDzData=xDzData,
\n yDzData=yDzData,
\n xPlData=xPlData,
\n yPlData=yPlData,
\n xZfData=xZfData,
\n yZfData=yZfData)<\/p>\n

@pb.route('commentDataAnalysis')
\ndef commentDataAnalysis():
\n """
\n \u5fae\u535a\u8bc4\u8bba\u6570\u636e\u5206\u6790
\n :return:
\n """
\n commentList = commentDao.getAllComment()
\n xDzData = [] # \u70b9\u8d5eX\u8f74\u6570\u636e
\n rangeNum = 5
\n for item in range(0, 20):
\n xDzData.append(str(rangeNum * item) + '-' + str(rangeNum * (item + 1)))
\n xDzData.append('1\u767e+')
\n yDzData = [0 for x in range(len(xDzData))] # \u70b9\u8d5ey\u8f74\u6570\u636e
\n genderDic = {'\u7537': 0, '\u5973': 0}
\n for comment in commentList:
\n for item in range(len(xDzData)):
\n if int(comment[4]) < rangeNum * (item + 1):
\n yDzData[item] += 1
\n break
\n elif int(comment[4]) > 100:
\n yDzData[len(xDzData) – 1] += 1
\n break
\n if genderDic.get(comment[8], -1) != -1:
\n genderDic[comment[8]] += 1
\n genderData = [{'name': x[0], 'value': x[1]} for x in genderDic.items()]<\/p>\n

# \u53ea\u8bfb\u53d6\u524d50\u6761\u6570\u636e
\n df = pd.read_csv('.\/fenci\/comment_fre.csv', nrows=50)
\n hotCommentwordList = [x[0] for x in df.values]
\n str2 = ' '.join(hotCommentwordList)
\n wordcloudUtil.genWordCloudPic(str2, 'comment_mask.jpg', 'comment_cloud.jpg')
\n return render_template('commentDataAnalysis.html',
\n xDzData=xDzData,
\n yDzData=yDzData,
\n genderData=genderData)<\/p>\n

@pb.route('articleCloud')
\ndef articleCloud():
\n """
\n \u5fae\u535a\u5185\u5bb9\u8bcd\u4e91\u56fe
\n :return:
\n """
\n # \u53ea\u8bfb\u53d6\u524d50\u6761\u6570\u636e
\n df = pd.read_csv('.\/fenci\/article_fre.csv', nrows=50)
\n hotArticlewordList = [x[0] for x in df.values]
\n str2 = ' '.join(hotArticlewordList)
\n wordcloudUtil.genWordCloudPic(str2, 'article_mask.jpg', 'article_cloud.jpg')
\n return render_template('articleCloud.html')<\/p>\n

@pb.route('commentCloud')
\ndef commentCloud():
\n """
\n \u5fae\u535a\u8bc4\u8bba\u8bcd\u4e91\u56fe
\n :return:
\n """
\n # \u53ea\u8bfb\u53d6\u524d50\u6761\u6570\u636e
\n df = pd.read_csv('.\/fenci\/comment_fre.csv', nrows=50)
\n hotCommentwordList = [x[0] for x in df.values]
\n str2 = ' '.join(hotCommentwordList)
\n wordcloudUtil.genWordCloudPic(str2, 'comment_mask.jpg', 'comment_cloud.jpg')
\n return render_template('commentCloud.html')<\/p>\n

@pb.route('commentUserCloud')
\ndef commentUserCloud():
\n """
\n \u5fae\u535a\u8bc4\u8bba\u7528\u6237\u8bcd\u4e91\u56fe
\n :return:
\n """
\n # \u83b7\u53d6top50\u8bc4\u8bba\u7528\u6237\u540d
\n top50CommentUserList = commentDao.getTopCommentUser()
\n top50CommentUserNameList = [cu[0] for cu in top50CommentUserList]
\n str = ' '.join(top50CommentUserNameList)
\n wordcloudUtil.genWordCloudPic(str, 'comment_mask.jpg', 'comment_user_cloud.jpg')
\n return render_template('commentUserCloud.html')<\/p>\n

@pb.route('ipDataAnalysis')
\ndef ipDataAnalysis():
\n """
\n IP\u5730\u5740\u6570\u636e\u5206\u6790
\n :return:
\n """
\n cityDic = {} # \u5fae\u535a\u6587\u7ae0\u4f5c\u8005IP
\n cityList = mapUtil.cityList
\n articleList = articleDao.getAllArticle()
\n for article in articleList:
\n if article[5]:
\n for city in cityList:
\n if city['province'].find(article[5]) != -1:
\n if cityDic.get(city['province'], -1) == -1:
\n cityDic[city['province']] = 1
\n else:
\n cityDic[city['province']] += 1
\n articleCityDicList = [{'name': x[0], 'value': x[1]} for x in cityDic.items()]<\/p>\n

cityDic2 = {} # \u5fae\u535a\u8bc4\u8bba\u4f5c\u8005IP
\n commentList = commentDao.getAllComment()
\n for comment in commentList:
\n if comment[3]:
\n for city in cityList:
\n if city['province'].find(comment[3]) != -1:
\n if cityDic2.get(city['province'], -1) == -1:
\n cityDic2[city['province']] = 1
\n else:
\n cityDic2[city['province']] += 1
\n commentCityDicList = [{'name': x[0], 'value': x[1]} for x in cityDic2.items()]
\n return render_template('ipDataAnalysis.html',
\n articleCityDicList=articleCityDicList,
\n commentCityDicList=commentCityDicList)<\/p>\n

@pb.route('sentimentAnalysis')
\ndef sentimentAnalysis():
\n """
\n \u8206\u60c5\u6570\u636e\u5206\u6790
\n :return:
\n """
\n xHotBarData = ['\u6b63\u9762', '\u8d1f\u9762']
\n yHotBarData = [0, 0]
\n # \u53ea\u8bfb\u53d6\u524d100\u6761
\n df = pd.read_csv('.\/fenci\/comment_fre.csv', nrows=100)
\n for value in df.values:
\n # \u60c5\u611f\u5206\u6790
\n # stc = SnowNLP(value[0]).sentiments
\n # if stc > 0.6:
\n # yHotBarData[0] += 1
\n # elif stc < 0.2:
\n # yHotBarData[2] += 1
\n # else:
\n # yHotBarData[1] += 1
\n # \u4f7f\u7528\u5927\u6a21\u578b\u8fdb\u884c\u60c5\u611f\u5206\u6790
\n # sentiment = data_classfication(value[0])
\n # \u4f7f\u7528\u5927\u6a21\u578b\u5fae\u8c03\u8fdb\u884c\u60c5\u611f\u5206\u6790
\n sentiment = check_data([value[0]])
\n if sentiment == '\u6b63\u9762':
\n yHotBarData[0] += 1
\n else:
\n yHotBarData[1] += 1<\/p>\n

hotTreeMapData = [{
\n 'name': xHotBarData[0],
\n 'value': yHotBarData[0]
\n }, {
\n 'name': xHotBarData[1],
\n 'value': yHotBarData[1]
\n }]<\/p>\n

commentPieData = [{
\n 'name': '\u6b63\u9762',
\n 'value': 0
\n }, {
\n 'name': '\u8d1f\u9762',
\n 'value': 0
\n }]
\n articlePieData = [{
\n 'name': '\u6b63\u9762',
\n 'value': 0
\n }, {
\n 'name': '\u8d1f\u9762',
\n 'value': 0
\n }]
\n commentList = commentDao.getAllComment()
\n # \u622a\u53d6\u524d1000\u6761\u6570\u636e \u6a21\u62df\u4e0b
\n commentList = commentList[:100]
\n for comment in commentList:
\n # \u60c5\u611f\u5206\u6790
\n # stc = SnowNLP(comment[1]).sentiments
\n # if stc > 0.6:
\n # commentPieData[0]['value'] += 1
\n # elif stc < 0.2:
\n # commentPieData[2]['value'] += 1
\n # else:
\n # commentPieData[1]['value'] += 1
\n # \u4f7f\u7528\u5927\u6a21\u578b\u8fdb\u884c\u60c5\u611f\u5206\u6790
\n # sentiment = data_classfication(comment[1])
\n # \u4f7f\u7528\u5927\u6a21\u578b\u5fae\u8c03\u8fdb\u884c\u60c5\u611f\u5206\u6790
\n sentiment = check_data([comment[1]])
\n if sentiment == '\u6b63\u9762':
\n commentPieData[0]['value'] += 1
\n else:
\n commentPieData[1]['value'] += 1<\/p>\n

articleList = articleDao.getAllArticle()
\n # \u83b7\u53d6\u524d1000\u6761\u6570\u636e \u6a21\u62df\u4e0b
\n articleList = articleList[:100]
\n for article in articleList:
\n # \u60c5\u611f\u5206\u6790
\n # stc = SnowNLP(article[1]).sentiments
\n # if stc > 0.6:
\n # articlePieData[0]['value'] += 1
\n # elif stc < 0.2:
\n # articlePieData[2]['value'] += 1
\n # else:
\n # articlePieData[1]['value'] += 1
\n # \u4f7f\u7528\u5927\u6a21\u578b\u8fdb\u884c\u60c5\u611f\u5206\u6790
\n # sentiment = data_classfication(article[1])
\n # \u4f7f\u7528\u5927\u6a21\u578b\u5fae\u8c03\u8fdb\u884c\u60c5\u611f\u5206\u6790
\n sentiment = check_data([article[1]])
\n if sentiment == '\u6b63\u9762':
\n articlePieData[0]['value'] += 1
\n else:
\n articlePieData[1]['value'] += 1<\/p>\n

df2 = pd.read_csv('.\/fenci\/comment_fre.csv', nrows=15)
\n xhotData15 = [x[0] for x in df2.values][::-1]
\n yhotData15 = [x[1] for x in df2.values][::-1]
\n return render_template('sentimentAnalysis.html',
\n xHotBarData=xHotBarData,
\n yHotBarData=yHotBarData,
\n hotTreeMapData=hotTreeMapData,
\n commentPieData=commentPieData,
\n articlePieData=articlePieData,
\n xhotData15=xhotData15,
\n yhotData15=yhotData15)<\/p>\n

#
\n# @pb.route('sentimentAnalysis')
\n# def sentimentAnalysis():
\n# """
\n# \u8206\u60c5\u6570\u636e\u5206\u6790
\n# :return:
\n# """
\n# xHotBarData = ['\u6b63\u9762', '\u8d1f\u9762']
\n# yHotBarData = [0, 0]
\n# # \u53ea\u8bfb\u53d6\u524d100\u6761
\n# df = pd.read_csv('.\/fenci\/comment_fre.csv', nrows=100)
\n# for value in df.values:
\n# # \u60c5\u611f\u5206\u6790
\n# # stc = SnowNLP(value[0]).sentiments
\n# # if stc > 0.6:
\n# # yHotBarData[0] += 1
\n# # elif stc < 0.2:
\n# # yHotBarData[2] += 1
\n# # else:
\n# # yHotBarData[1] += 1
\n# # \u4f7f\u7528\u5927\u6a21\u578b\u8fdb\u884c\u60c5\u611f\u5206\u6790
\n# sentiment = data_classfication(value[0])
\n# if sentiment == '\u6b63\u9762':
\n# yHotBarData[0] += 1
\n# else:
\n# yHotBarData[1] += 1
\n#
\n# hotTreeMapData = [{
\n# 'name': xHotBarData[0],
\n# 'value': yHotBarData[0]
\n# }, {
\n# 'name': xHotBarData[1],
\n# 'value': yHotBarData[1]
\n# }]
\n#
\n# commentPieData = [{
\n# 'name': '\u6b63\u9762',
\n# 'value': 0
\n# }, {
\n# 'name': '\u8d1f\u9762',
\n# 'value': 0
\n# }]
\n# articlePieData = [{
\n# 'name': '\u6b63\u9762',
\n# 'value': 0
\n# }, {
\n# 'name': '\u8d1f\u9762',
\n# 'value': 0
\n# }]
\n# commentList = commentDao.getAllComment()
\n# for comment in commentList:
\n# # \u60c5\u611f\u5206\u6790
\n# # stc = SnowNLP(comment[1]).sentiments
\n# # if stc > 0.6:
\n# # commentPieData[0]['value'] += 1
\n# # elif stc < 0.2:
\n# # commentPieData[2]['value'] += 1
\n# # else:
\n# # commentPieData[1]['value'] += 1
\n# # \u4f7f\u7528\u5927\u6a21\u578b\u8fdb\u884c\u60c5\u611f\u5206\u6790
\n# sentiment = data_classfication(comment[1])
\n# if sentiment == '\u6b63\u9762':
\n# commentPieData[0]['value'] += 1
\n# else:
\n# commentPieData[1]['value'] += 1
\n#
\n# articleList = articleDao.getAllArticle()
\n# for article in articleList:
\n# # \u60c5\u611f\u5206\u6790
\n# # stc = SnowNLP(article[1]).sentiments
\n# # if stc > 0.6:
\n# # articlePieData[0]['value'] += 1
\n# # elif stc < 0.2:
\n# # articlePieData[2]['value'] += 1
\n# # else:
\n# # articlePieData[1]['value'] += 1
\n# # \u4f7f\u7528\u5927\u6a21\u578b\u8fdb\u884c\u60c5\u611f\u5206\u6790
\n# sentiment = data_classfication(article[1])
\n# if sentiment == '\u6b63\u9762':
\n# articlePieData[0]['value'] += 1
\n# else:
\n# articlePieData[1]['value'] += 1
\n#
\n# df2 = pd.read_csv('.\/fenci\/comment_fre.csv', nrows=15)
\n# xhotData15 = [x[0] for x in df2.values][::-1]
\n# yhotData15 = [x[1] for x in df2.values][::-1]
\n# return render_template('sentimentAnalysis.html',
\n# xHotBarData=xHotBarData,
\n# yHotBarData=yHotBarData,
\n# hotTreeMapData=hotTreeMapData,
\n# commentPieData=commentPieData,
\n# articlePieData=articlePieData,
\n# xhotData15=xhotData15,
\n# yhotData15=yhotData15)<\/p>\n

#
\n# @pb.route('sentimentAnalysis')
\n# def sentimentAnalysis():
\n# """
\n# \u8206\u60c5\u6570\u636e\u5206\u6790
\n# :return:
\n# """
\n# xHotBarData = ['\u6b63\u9762', '\u4e2d\u6027', '\u8d1f\u9762']
\n# yHotBarData = [0, 0, 0]
\n# # \u53ea\u8bfb\u53d6\u524d100\u6761
\n# df = pd.read_csv('.\/fenci\/comment_fre.csv', nrows=100)
\n# for value in df.values:
\n# # \u60c5\u611f\u5206\u6790
\n# stc = SnowNLP(value[0]).sentiments
\n# if stc > 0.6:
\n# yHotBarData[0] += 1
\n# elif stc < 0.2:
\n# yHotBarData[2] += 1
\n# else:
\n# yHotBarData[1] += 1
\n#
\n# hotTreeMapData = [{
\n# 'name': xHotBarData[0],
\n# 'value': yHotBarData[0]
\n# }, {
\n# 'name': xHotBarData[1],
\n# 'value': yHotBarData[1]
\n# }, {
\n# 'name': xHotBarData[2],
\n# 'value': yHotBarData[2]
\n# }]
\n#
\n# commentPieData = [{
\n# 'name': '\u6b63\u9762',
\n# 'value': 0
\n# }, {
\n# 'name': '\u4e2d\u6027',
\n# 'value': 0
\n# }, {
\n# 'name': '\u8d1f\u9762',
\n# 'value': 0
\n# }]
\n# articlePieData = [{
\n# 'name': '\u6b63\u9762',
\n# 'value': 0
\n# }, {
\n# 'name': '\u4e2d\u6027',
\n# 'value': 0
\n# }, {
\n# 'name': '\u8d1f\u9762',
\n# 'value': 0
\n# }]
\n# commentList = commentDao.getAllComment()
\n# for comment in commentList:
\n# # \u60c5\u611f\u5206\u6790
\n# stc = SnowNLP(comment[1]).sentiments
\n# if stc > 0.6:
\n# commentPieData[0]['value'] += 1
\n# elif stc < 0.2:
\n# commentPieData[2]['value'] += 1
\n# else:
\n# commentPieData[1]['value'] += 1
\n#
\n# articleList = articleDao.getAllArticle()
\n# for article in articleList:
\n# # \u60c5\u611f\u5206\u6790
\n# stc = SnowNLP(article[1]).sentiments
\n# if stc > 0.6:
\n# articlePieData[0]['value'] += 1
\n# elif stc < 0.2:
\n# articlePieData[2]['value'] += 1
\n# else:
\n# articlePieData[1]['value'] += 1
\n#
\n# df2 = pd.read_csv('.\/fenci\/comment_fre.csv', nrows=15)
\n# xhotData15 = [x[0] for x in df2.values][::-1]
\n# yhotData15 = [x[1] for x in df2.values][::-1]
\n# return render_template('sentimentAnalysis.html',
\n# xHotBarData=xHotBarData,
\n# yHotBarData=yHotBarData,
\n# hotTreeMapData=hotTreeMapData,
\n# commentPieData=commentPieData,
\n# articlePieData=articlePieData,
\n# xhotData15=xhotData15,
\n# yhotData15=yhotData15)<\/p>\n","protected":false},"excerpt":{"rendered":"

\u5927\u5bb6\u597d，\u6211\u662f\u950b\u54e5，\u6700\u8fd1\u5199\u4e86AI\u5927\u6a21\u578b\u5fae\u8c03\u8bad\u7ec3 \u5fae\u535a\u8206\u60c5\u5206\u6790\u53ef\u89c6\u5316\u7cfb\u7edf(pytorch2\u57fa\u4e8eBERT\u5927\u6a21\u578b\u8bad\u7ec3\u5fae\u8c03flaskpandasecharts)，\u975e\u5e38Nice，\u754c\u9762\u975e\u5e38\u597d\u770b，\u5206\u4eab\u4e0b\u54c8\u3002\u9879\u76ee\u4ecb\u7ecd\u672c\u6587\u8bbe\u8ba1\u5e76\u5b9e\u73b0\u4e86\u4e00\u4e2a\u57fa\u4e8eBERT\u5927\u6a21\u578b\u5fae\u8c03\u8bad\u7ec3\u7684\u5fae\u535a\u8206\u60c5\u5206\u6790\u53ef\u89c6\u5316\u7cfb\u7edf\u3002\u7cfb\u7edf\u91c7\u7528Python\u8bed\u8a00\u5f00\u53d1，\u540e\u7aef\u4f7f\u7528Flask Web\u6846\u67b6，\u524d\u7aef\u4f7f\u7528ECharts\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316&#xff0c<\/p>\n","protected":false},"author":2,"featured_media":77471,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[8510,2554,153,50,132],"topic":[],"class_list":["post-77479","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-8510","tag-bert","tag-flask","tag-50","tag-132"],"yoast_head":"\n\u5206\u4eab\u4e00\u5957\u950b\u54e5\u539f\u521b\u7684AI\u5927\u6a21\u578b\u5fae\u8c03\u8bad\u7ec3 \u5fae\u535a\u8206\u60c5\u5206\u6790\u53ef\u89c6\u5316\u7cfb\u7edf(pytorch2+\u57fa\u4e8eBERT\u5927\u6a21\u578b\u8bad\u7ec3\u5fae\u8c03+flask+pandas+echarts)\uff0c\u975e\u5e38Nice\uff0c\u754c\u9762\u975e\u5e38\u597d\u770b - 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